View Synthesis by Trinocular Edge Matching and Transfer

1998 ◽  
Author(s):  
S. Pollard ◽  
M. Pilu ◽  
S. Hayes ◽  
A. Lorusso
Keyword(s):  
View Synthesis ◽  
Edge Matching

View synthesis by trinocular edge matching and transfer

2000 ◽  
Vol 18 (9) ◽  
pp. 749-757 ◽  
Author(s):  
S. Pollard ◽  
M. Pilu ◽  
S. Hayes ◽  
A. Lorusso
Keyword(s):  
View Synthesis ◽  
Edge Matching

Object-centered View Synthesis using Learning-based Image Inpainting

2019 ◽  
pp. 154
Author(s):  
HONG-CHANG SHIN ◽  
Gwangsoon Lee ◽  
Ho min Eum ◽  
Jeong-Il Seo
Keyword(s):  
Image Inpainting ◽  
View Synthesis

scholarly journals A Disparity Refinement Algorithm for Satellite Remote Sensing Images Based on Mean-Shift Plane Segmentation

2021 ◽  
Vol 13 (10) ◽  
pp. 1903
Author(s):  
Zhihui Li ◽  
Jiaxin Liu ◽  
Yang Yang ◽  
Jing Zhang
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Stereo Matching ◽  
Mean Shift ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Edge Matching ◽  
Refinement Method ◽  
Initial Segmentation ◽  
Refinement Algorithm

Objects in satellite remote sensing image sequences often have large deformations, and the stereo matching of this kind of image is so difficult that the matching rate generally drops. A disparity refinement method is needed to correct and fill the disparity. A method for disparity refinement based on the results of plane segmentation is proposed in this paper. The plane segmentation algorithm includes two steps: Initial segmentation based on mean-shift and alpha-expansion-based energy minimization. According to the results of plane segmentation and fitting, the disparity is refined by filling missed matching regions and removing outliers. The experimental results showed that the proposed plane segmentation method could not only accurately fit the plane in the presence of noise but also approximate the surface by plane combination. After the proposed plane segmentation method was applied to the disparity refinement of remote sensing images, many missed matches were filled, and the elevation errors were reduced. This proved that the proposed algorithm was effective. For difficult evaluations resulting from significant variations in remote sensing images of different satellites, the edge matching rate and the edge matching map are proposed as new stereo matching evaluation and analysis tools. Experiment results showed that they were easy to use, intuitive, and effective.

Passthrough+

2020 ◽  
Vol 3 (1) ◽  
pp. 1-17
Author(s):  
Gaurav Chaurasia ◽  
Arthur Nieuwoudt ◽  
Alexandru-Eugen Ichim ◽  
Richard Szeliski ◽  
Alexander Sorkine-Hornung
Keyword(s):  
Real Time ◽  
Video Encoding ◽  
View Synthesis ◽  
Performance Constraints ◽  
Trade Offs ◽  
Stereoscopic View ◽  
Target User ◽  
Spatio Temporal ◽  
And Performance ◽  
3D Scene

We present an end-to-end system for real-time environment capture, 3D reconstruction, and stereoscopic view synthesis on a mobile VR headset. Our solution allows the user to use the cameras on their VR headset as their eyes to see and interact with the real world while still wearing their headset, a feature often referred to as Passthrough. The central challenge when building such a system is the choice and implementation of algorithms under the strict compute, power, and performance constraints imposed by the target user experience and mobile platform. A key contribution of this paper is a complete description of a corresponding system that performs temporally stable passthrough rendering at 72 Hz with only 200 mW power consumption on a mobile Snapdragon 835 platform. Our algorithmic contributions for enabling this performance include the computation of a coarse 3D scene proxy on the embedded video encoding hardware, followed by a depth densification and filtering step, and finally stereoscopic texturing and spatio-temporal up-sampling. We provide a detailed discussion and evaluation of the challenges we encountered, as well as algorithm and performance trade-offs in terms of compute and resulting passthrough quality.;AB@The described system is available to users as the Passthrough+ feature on Oculus Quest. We believe that by publishing the underlying system and methods, we provide valuable insights to the community on how to design and implement real-time environment sensing and rendering on heavily resource constrained hardware.

Unsupervised Stereoscopic Image Retargeting via View Synthesis and Stereo Cycle Consistency Losses

2021 ◽  
Author(s):  
Xiaoting Fan ◽  
Jianjun Lei ◽  
Jie Liang ◽  
Yuming Fang ◽  
Xiaochun Cao ◽  
...  
Keyword(s):  
View Synthesis ◽  
Stereoscopic Image ◽  
Image Retargeting

Disocclusion-type aware hole filling method for view synthesis

2021 ◽  
Author(s):  
Xiaodong Chen ◽  
Haitao Liang ◽  
Huaiyuan Xu ◽  
Siyu Ren ◽  
Huaiyu Cai ◽  
...  
Keyword(s):  
View Synthesis ◽  
Hole Filling ◽  
Filling Method
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